Deep Learning-based Transfer Learning for Classification of Skin Cancer

The majority of cancer deaths are caused by metastases, which occur when circulating tumour cells (CTCs) migrate from the main tumour to important organs. Current methods for detecting Cells as identifiers of tumor growth are inefficient and requires numerous sample processing steps such as red blood cell transfer, labelling, analyte encapsulate, and separation, that can result in CTC decrease. This research presents a novel method for detecting and identifying CTCs that employs absorption spectra imaging of tumor tissue as well as spectral clearing of samples. This method was tested using human melanoma cell phantoms and ejections of mammalian malignant cells of strains and combination with blood. Using optical clearing of mouse blood, the strategy of increasing accuracy was illustrated. Chemical agents that are biocompatible. The findings impacts that the proposed diagnostic platform could detect Cells in melanoma clients' blood samples rapidly.